OpenRaiser/Intern-Atlas huggingface dataset:
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

Progress toward the United Nations Sustainable Development Goals (SDGs) has been hindered by a lack of data on key environmental and socioeconomic indicators, which historically have come from ground surveys with sparse temporal and spatial coverage. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media, to provide insights into progress toward SDGs. Despite promising early results, approaches to using such data for SDG measurement thus far have largely evaluated on different datasets or used inconsistent evaluation metrics, making it hard to understand whether performance is improving and where additional research would be most fruitful. Furthermore, processing satellite and ground survey data requires domain knowledge that many in the machine learning community lack. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs, including tasks related to economic development, agriculture, health, education, water and sanitation, climate action, and life on land. Datasets for 11 of the 15 tasks are released publicly for the first time. Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
Survey of Deep Learning Methods for Inverse Problems
Venue: arXiv cs.CV 2021
Year: 2021

In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three categories of Direct Mapping, Data Consistency Optimizer, and Deep Regularizer. We choose a sample of each inverse problem type, so as to compare the robustness of the three categories, and report a statistical analysis of their differences. We perform extensive experiments on the classic problem of linear regression and three well-known inverse problems in computer vision, namely image denoising, 3D human face inverse rendering, and object tracking, selected as representative prototypes for each class of inverse problems. The overall results and the statistical analyses show that the solution categories have a robustness behaviour dependent on the type of inverse problem domain, and specifically dependent on whether or not the problem includes measurement outliers. Based on our experimental results, we conclude by proposing the most robust solution category for each inverse problem class.
Lipschitz regularization for fracture: the Lip-field approach
Venue: arXiv cs.CE 2021
Year: 2021

The Lip-field approach is a new regularization method for softening material material models. It was presented first in a previous paper providing one-dimensional simulations for damage and plasticity. The present paper focuses on a two-dimensional implementation for elasto-damage models (quasi-brittle fracture). The incremental potential used in the Lip-field approach is the non-regularized one. The regularization comes from the addition of a Lipschitz constraint on the damage field. In other words, the free energy does not depend on the damage gradient. The search of the displacement and damage fields from one time-step to the next is based on an iterative staggered scheme. The displacement field is sought for a given damage field. Then, a Lipschitz continuous damage field is sought for a given displacement field. Both problems are convex. The solution to the latter benefits from bounds proven in a previous paper and used here. The paper details the implementation of the Lipschitz regularity on a finite element mesh and details the overall solution scheme. Four numerical examples demonstrate the capability of the new approach.
Frustum Fusion: Pseudo-LiDAR and LiDAR Fusion for 3D Detection
Venue: arXiv cs.CV 2021
Year: 2021

Most autonomous vehicles are equipped with LiDAR sensors and stereo cameras. The former is very accurate but generates sparse data, whereas the latter is dense, has rich texture and color information but difficult to extract robust 3D representations from. In this paper, we propose a novel data fusion algorithm to combine accurate point clouds with dense but less accurate point clouds obtained from stereo pairs. We develop a framework to integrate this algorithm into various 3D object detection methods. Our framework starts with 2D detections from both of the RGB images, calculates frustums and their intersection, creates Pseudo-LiDAR data from the stereo images, and fills in the parts of the intersection region where the LiDAR data is lacking with the dense Pseudo-LiDAR points. We train multiple 3D object detection methods and show that our fusion strategy consistently improves the performance of detectors.
Improved security solutions for DDoS mitigation in 5G Multi-access Edge Computing
Venue: arXiv cs.CR 2021
Year: 2021

Multi-access Edge Computing (MEC) is a 5G-enabling solution that aims to bring cloud-computing capabilities closer to the end-users. This paper focuses on mitigation techniques against Distributed Denial-of-Service (DDoS) attacks in the context of 5G MEC, providing solutions that involve the virtualized environment and the management entities from the MEC architecture. The proposed solutions aim to reduce the risk of affecting legitimate traffic in the context of DDoS attacks. Our work supports the idea of using a network flow collector that sends the data to an anomaly detection system based on artificial intelligence techniques and, as an improvement over the previous work, it contributes to redirecting detected anomalies for isolation to a separate virtual machine. This virtual machine uses deep packet inspection tools to analyze the traffic and provides services until the final verdict. We decrease the risk of compromising the virtual machine that provides services to legitimate users by isolating the suspicious traffic. The management entities of the MEC architecture allow to re-instantiate or reconfigure the virtual machines. Hence, if the machine inspecting the isolated traffic crashes because of an attack, the damaged machine can be restored while the services provided to legitimate users are not affected.
Creative Compensation (CC): Future of Jobs with Creative Works in 3D Printing
Venue: arXiv cs.HC 2021
Year: 2021

With the continuous growth of online 3D printing community and the democratization of 3D printers, growing number of users start sharing their own 3D designs on open platforms, enabling a wide audience to search, download, and 3D print models for free. Although sharing is mostly for altruistic reasons at first, open platforms had also created potential job opportunities to compensate creative labors. This paper analyzes new job opportunities emerged in online 3D printing social platforms and patterns of seeking compensations, and reveals various motivations for posting creative content online. We find that offering exclusive membership through subscriptions, selling final products or printing services through web stores, and using affiliate links are primary means of earning profits, while there exist gaps between creators' expectations and realities. We show that various socio-economic promises emerged, leading to a win-win situation for both creators to gain extra income and audiences to have access to more quality content. We also discuss future challenges that need to be addressed, such as ethical use of opensource content.
D$^2$ABS: A Framework for Dynamic Dependence Abstraction of Distributed Programs
Venue: arXiv cs.SE 2021
Year: 2021

As modern software systems are increasingly developed for running in distributed environments, it is crucial to provide fundamental techniques such as dependence analysis for checking, diagnosing, and evolving those systems. However, traditional dependence analysis is either inapplicable or of very limited utility for distributed programs due to the decoupled components of these programs that run in concurrent processes at physically separated machines. Motivated by the need for dependence analysis of distributed software and the diverse cost-effectiveness needs of dependence-based applications, this paper presents D$^2$ABS, a framework of dynamic dependence abstraction for distributed programs. By partial-ordering distributed method-execution events and inferring causality from the ordered events, D$^2$ABS abstracts method-level dependencies both within and across process boundaries. Further, by exploiting message-passing semantics across processes, and incorporating static dependencies and statement coverage within individual components, we present three additional instantiations of D$^2$ABS that trade efficiency for better precision. We present the design of the D$^2$ABS framework and evaluate the four instantiations of D$^2$ABS on distributed systems of various architectures and scales using our implementation for Java. Our empirical results show that D$^2$ABS is significantly more effective than existing options while offering varied levels of cost-effectiveness tradeoffs. As our framework essentially computes whole-system run-time dependencies, it naturally empowers a range of other dependence-based applications.
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods
Venue: arXiv cs.LG 2021
Year: 2021

The increasing adoption of Reinforcement Learning in safety-critical systems domains such as autonomous vehicles, health, and aviation raises the need for ensuring their safety. Existing safety mechanisms such as adversarial training, adversarial detection, and robust learning are not always adapted to all disturbances in which the agent is deployed. Those disturbances include moving adversaries whose behavior can be unpredictable by the agent, and as a matter of fact harmful to its learning. Ensuring the safety of critical systems also requires methods that give formal guarantees on the behaviour of the agent evolving in a perturbed environment. It is therefore necessary to propose new solutions adapted to the learning challenges faced by the agent. In this paper, first we generate adversarial agents that exhibit flaws in the agent's policy by presenting moving adversaries. Secondly, We use reward shaping and a modified Q-learning algorithm as defense mechanisms to improve the agent's policy when facing adversarial perturbations. Finally, probabilistic model checking is employed to evaluate the effectiveness of both mechanisms. We have conducted experiments on a discrete grid world with a single agent facing non-learning and learning adversaries. Our results show a diminution in the number of collisions between the agent and the adversaries. Probabilistic model checking provides lower and upper probabilistic bounds regarding the agent's safety in the adversarial environment.
Structure-Behavior Coalescence Process Algebra -- Toward a Unified View of the System in Model-Based Systems Engineering
Venue: arXiv cs.SE 2021
Year: 2021

In Model-Based Systems Engineering (MBSE), the Systems Modeling Language (SysML) specification includes a metamodel that defines the language concepts and a user model that defines how the language concepts are represented. In SysML, an important use of metamodel is to provide an integrated semantic framework that every diagram in the user model can be projected as a view of the metamodel. However, most existing SysML metamodels lack such capability of being a basis for unification of different views of a system. To overcome the shortcomings of the current SysML metamodel approaches, we developed Channel-Based Multi-Queue Structure-Behavior Coalescence Process Algebra (C-M-SBC-PA), which provides an integrated semantic framework that is able to integrate structural constructs with behavioral constructs. Using C-M-SBC-PA as the metamodel of SysML, each diagram in the user model can be projected as a view of the C-M-SBC-PA metamodel.
vlang: Mapping Verilog Netlists to Modern Technologies
Venue: arXiv cs.AR 2021
Year: 2021

Portability of hardware designs between Programmable Logic Devices (PLD) can be accomplished through the use of device-agnostic hardware description languages (HDL) such as Verilog or VHDL. Hardware designers can use HDLs to migrate hardware designs between devices and explore performance, area and power tradeoffs, as well as, port designs to an alternative device. However, if design files are corrupt or missing, the portability of the design is lost. While reverse engineering efforts may be able to recover an HDL-netlist of the original design, HDL-netlists use device-specific primitives, restricting portability. Additionally, the recovered design may benefit from other computational technologies (e.g., $μ$P, GPGPUs), but is restricted to the domain of PLDs. In this work, we provide a new framework, vlang, which automatically maps Verilog-netlists into LLVM's intermediate representation (IR). The remapped design can use the LLVM-framework to target many device technologies such as: x86-64 assembly, RISC-V, ARM or to other PLDs with a modern high-level synthesis tool. Our framework is able to preserve the exact functionality of the original design within the software executable. The vlang-produced software executable can be used with other software programs, or to verify the functionality and correctness of the remapped design. We evaluate our work with a suite of hardware designs from OpenCores. We compare our framework against state-of-the-art simulators, thereby outlining our framework's ability to produce a fully-functional, cycle accurate software-executable. We also explore the usage of vlang as a front-end for high-level synthesis tools.
On the structure of $1$-generator quasi-polycyclic codes over finite chain rings
Venue: arXiv cs.IT 2021
Year: 2021

Quasi-polycyclic (QP for short) codes over a finite chain ring $R$ are a generalization of quasi-cyclic codes, and these codes can be viewed as an $R[x]$-submodule of $\mathcal{R}_m^{\ell}$, where $\mathcal{R}_m:= R[x]/\langle f\rangle$, and $f$ is a monic polynomial of degree $m$ over $R$. If $f$ factors uniquely into monic and coprime basic irreducibles, then their algebraic structure allow us to characterize the generator polynomials and the minimal generating sets of 1-generator QP codes as $R$-modules. In addition, we also determine the parity check polynomials for these codes by using the strong Gröbner bases. In particular, via Magma system, some quaternary codes with new parameters are derived from these 1-generator QP codes.
DSBERT:Unsupervised Dialogue Structure learning with BERT
Venue: arXiv cs.CL 2021
Year: 2021

Unsupervised dialogue structure learning is an important and meaningful task in natural language processing. The extracted dialogue structure and process can help analyze human dialogue, and play a vital role in the design and evaluation of dialogue systems. The traditional dialogue system requires experts to manually design the dialogue structure, which is very costly. But through unsupervised dialogue structure learning, dialogue structure can be automatically obtained, reducing the cost of developers constructing dialogue process. The learned dialogue structure can be used to promote the dialogue generation of the downstream task system, and improve the logic and consistency of the dialogue robot's reply.In this paper, we propose a Bert-based unsupervised dialogue structure learning algorithm DSBERT (Dialogue Structure BERT). Different from the previous SOTA models VRNN and SVRNN, we combine BERT and AutoEncoder, which can effectively combine context information. In order to better prevent the model from falling into the local optimal solution and make the dialogue state distribution more uniform and reasonable, we also propose three balanced loss functions that can be used for dialogue structure learning. Experimental results show that DSBERT can generate a dialogue structure closer to the real structure, can distinguish sentences with different semantics and map them to different hidden states.
PREMA: Part-based REcurrent Multi-view Aggregation Network for 3D Shape Retrieval
Venue: arXiv cs.CV 2021
Year: 2021

We propose the Part-based Recurrent Multi-view Aggregation network(PREMA) to eliminate the detrimental effects of the practical view defects, such as insufficient view numbers, occlusions or background clutters, and also enhance the discriminative ability of shape representations. Inspired by the fact that human recognize an object mainly by its discriminant parts, we define the multi-view coherent part(MCP), a discriminant part reoccurring in different views. Our PREMA can reliably locate and effectively utilize MCPs to build robust shape representations. Comprehensively, we design a novel Regional Attention Unit(RAU) in PREMA to compute the confidence map for each view, and extract MCPs by applying those maps to view features. PREMA accentuates MCPs via correlating features of different views, and aggregates the part-aware features for shape representation.
DataX: A system for Data eXchange and transformation of streams
Venue: arXiv cs.DC 2021
Year: 2021

The exponential growth in smart sensors and rapid progress in 5G networks is creating a world awash with data streams. However, a key barrier to building performant multi-sensor, distributed stream processing applications is high programming complexity. We propose DataX, a novel platform that improves programmer productivity by enabling easy exchange, transformations, and fusion of data streams. DataX abstraction simplifies the application's specification and exposes parallelism and dependencies among the application functions (microservices). DataX runtime automatically sets up appropriate data communication mechanisms, enables effortless reuse of microservices and data streams across applications, and leverages serverless computing to transform, fuse, and auto-scale microservices. DataX makes it easy to write, deploy and reliably operate distributed applications at scale. Synthesizing these capabilities into a single platform is substantially more transformative than any available stream processing system.
Evaluation Of Orthogonal Chirp Division Multiplexing For Automotive Integrated Sensing And Communications
Venue: arXiv cs.IT 2021
Year: 2021

We consider a bistatic vehicular integrated sensing and communications (ISAC) system that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multicarrier waveform. As a stand-alone communications waveform, OCDM has been shown to be robust against the interference in time-frequency selective channels. In a bistatic ISAC, we exploit this property to develop efficient receive processing algorithms that achieve high target resolution as well as high communications rate. We derive statistical bounds for our proposed Sequential symbol decoding and radar parameter estimation (SUNDAE) algorithm and compare its competitive performance with other multicarrier waveforms through numerical experiments.
Cryptanalyze and design strong S-Box using 2D chaotic map and apply to irreversible key expansion
Venue: arXiv cs.CR 2021
Year: 2021

Cryptanalysis result of key expansion algorithms in AES and SM4 revealed that, (1) there exist weaknesses in their S-Boxes, and (2) the round key expansion algorithm is reversible, i.e., the initial key can be recovered from any round key, which may be an exploitable weakness by attacker. To solve these problems, first we constructed a non-degenerate 2D exponential hyper chaotic map (2D-ECM), derived the recursion formula to calculate the number of S-Boxes that satisfied three conditions, and designed a strong S-Box construction algorithm without weakness. Then based on 2D-ECM and S-Box, we designed an irreversible key expansion algorithm, to transform the initial key into independent round keys, to make the initial key can not be recovered from any round key. Security and statistical analysis demonstrated the flexible and effectiveness of the proposed irreversible key expansion algorithm.
Multimodal intelligibility of scholarly hypertext: the documentalist's contribution. A required collaboration for serial documentisation in the scientific editorial process
Venue: arXiv cs.CL 2021
Year: 2021

This article shows that the boundaries between the editing and online publishingprofessions are losing their strength. In this context it would only make sense that the wayhypertexts are documented be renewed, especially facing of the Web's evolution. We arethinking in particular of the trickier scholar hypertexts documentation process - specifically inscientific or cultural contexts. The purpose of this article is to demonstrate that, consideringthe numerous branches of the Web, the hypertext enhance of a document of quality can onlybe done through a proper dialogue between authors, editors, and broadcasters. It would satisfythe readership as they could reach the appropriate information. It will also be shown that eachactor in this auctorial-editorial process would be a gainer. Indeed, a qualitative formalizationwork would be coupled with a strong broadcasting scope. Finally, we will point out that thiswork of mediating must be led by an actor of information-communication, to make the textunderstandable to both humans and machines. This meditative act is designated here under theterm of serial documentarisation.
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly
Venue: arXiv cs.CV 2021
Year: 2021

A significant number of people are suffering from cognitive impairment all over the world. Early detection of cognitive impairment is of great importance to both patients and caregivers. However, existing approaches have their shortages, such as time consumption and financial expenses involved in clinics and the neuroimaging stage. It has been found that patients with cognitive impairment show abnormal emotion patterns. In this paper, we present a novel deep convolution network-based system to detect the cognitive impairment through the analysis of the evolution of facial emotions while participants are watching designed video stimuli. In our proposed system, a novel facial expression recognition algorithm is developed using layers from MobileNet and Support Vector Machine (SVM), which showed satisfactory performance in 3 datasets. To verify the proposed system in detecting cognitive impairment, 61 elderly people including patients with cognitive impairment and healthy people as a control group have been invited to participate in the experiments and a dataset was built accordingly. With this dataset, the proposed system has successfully achieved the detection accuracy of 73.3%.